MolReasoner：迈向分子大语言模型的有效且可解释的推理
MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
AI总结：
MolReasoner通过两阶段框架提升分子大语言模型的推理能力和可解释性，有效减少幻觉并提高生成质量。
摘要：
大型语言模型（LLMs）在各种领域展现出卓越的性能，但其进行分子推理的能力仍处于探索阶段。现有方法大多依赖通用提示，缺乏特定分子语义，或微调，面临可解释性和推理深度的挑战，往往导致结构和文本幻觉。为了解决这些问题，我们引入了MolReasoner，一个两阶段框架，将LLMs从记忆转换为高保真化学推理。在Mol-SFT阶段，知识增强的思维链（CoT）数据提供了坚实的基础，而Mol-RL阶段利用新颖的任务自适应奖励系统来减轻幻觉。广泛的评估显示，MolReasoner在分子生成和描述任务中显著优于广泛的强基线。进一步分析突显了该框架的协同设计及其产生更可解释输出的能力。我们的工作提出了一个原则性且有效的新型方法，以推进高保真分子推理。
英文摘要：
Large Language Models (LLMs) have shown impressive performance across various domains, but their ability to perform molecular reasoning remains underexplored. Existing methods mostly rely on general-purpose prompting, which lacks domain-specific molecular semantics, or fine-tuning, which faces challenges in interpretability and reasoning depth, often leading to structural and textual hallucinations. To address these issues, we introduce MolReasoner, a two-stage framework that transitions LLMs from memorization to high-fidelity chemical reasoning. In the Mol-SFT stage, knowledge-enhanced Chain-of-Thought (CoT) data provides a strong foundation, while the Mol-RL stage refines reasoning using a novel, task-adaptive reward system to mitigate hallucinations. Extensive evaluations demonstrate that MolReasoner significantly outperforms a wide range of strong baselines in both molecule generation and captioning tasks. Further analyses highlight the framework's synergistic design and its ability to produce more interpretable outputs. Our work presents a principled and effective new approach for advancing high-fidelity molecular reasoning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
